Highlights  Cellular metabolism and toxicity of TFAs are still to be elucidated.  TFAs were incorporated in RINm5F insulinoma cells like palmitate or oleate.  Similarly to oleate and unlike palmitate, TFAs were of mild toxicity.  FA-induced cell damage correlated with ceramide and diglyceride accumulation.  Incorporation of TFAs in ceramides and diglycerides exceeded that of oleate.

A sensitive, reproducible reverse-phased high performance liquid chromatography electrospray tandem mass spectrometry (HPLC-ESI-MS/MS) method with simple sample preparation was developed for the simultaneous determination of a wide range of ceramides, diacylglycerols (DAGs) in cultured cells. Chromatographic separation of the compounds was achieved in a 14-minute run using a C8 column with a gradient elution by methanol and 10 mM ammonium acetate buffer as mobile phase at a flow rate of 0.5 ml/min. Various ceramides, DAGs were detected with a triple quadrupol system in multiple reaction monitoring mode, which is based on a soft positive electrospray ionization. The usual sample preparation process was shortened by the application of pure methanol for the extraction instead of the widely used methanol/chloroform mixture. C17:0 ceramide which does not occur in the cell samples, was used as an internal standard. The sample preparation process was optimized and the methodology was tested on a human hepatocarcinoma cell culture. Our results clearly showed accumulation of some ceramides and DAGs in the cells treated with BSA-conjugated palmitate for 8 hours. Since both ceramides and DAGs are important lipid intermediates and signal messengers, alteration in their cellular levels have major impact on cell functions, and thus our novel analytic method can be widely used in lipotoxicity research. The presented technique can be further developed to measure other intermediates of ceramide synthesis and other derivatives of DAGs as well.

Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their 1H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO2 concentrations. All the models performed successfully, with R2 values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. 1H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science.